Content-Based Re-ranking of Text-Based Image Search Results

This article presents a method for re-ranking images retrieved by classical search engine using key words for entering queries. This method uses the visual content of the images and it is based on the idea that the relevant images should be similar to each other while the non-relevant images should be different from each other and from relevant images. This idea has been implemented by ranking the images according to their average distances to their nearest neighbors. This query-dependent re-ranking is completed by a query-independent re-ranking taking into account the fact that some types of images are non-relevant for almost all queries. This idea is implemented by training a classifier on results from all queries in the training set. The re-ranking is successfully evaluated on classical datasets built with ExaleadTM and Google ImagesTM search engines.

[1]  Peter Ingwersen,et al.  Developing a Test Collection for the Evaluation of Integrated Search , 2010, ECIR.

[2]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Antonio Criminisi,et al.  Harvesting Image Databases from the Web , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[4]  Shumeet Baluja,et al.  VisualRank: Applying PageRank to Large-Scale Image Search , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Edwin R. Hancock,et al.  Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings , 2010, SSPR/SPR.

[6]  Xiaoou Tang,et al.  Real time google and live image search re-ranking , 2008, ACM Multimedia.

[7]  Bernhard Schölkopf,et al.  Ranking on Data Manifolds , 2003, NIPS.

[8]  Rong Jin,et al.  Web image retrieval re-ranking with relevance model , 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

[9]  Avi Arampatzis,et al.  Dynamic Two-Stage Image Retrieval from Large Multimodal Databases , 2011, ECIR.

[10]  Hervé Glotin,et al.  IRIM at TRECVID2009: High Level Feature Extraction , 2009 .

[11]  Carol Peters,et al.  Evaluating Systems for Multilingual and Multimodal Information Access, 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Aarhus, Denmark, September 17-19, 2008, Revised Selected Papers , 2009, CLEF.

[12]  Vidit Jain,et al.  Learning to re-rank: query-dependent image re-ranking using click data , 2011, WWW.

[13]  Frédéric Jurie,et al.  Improving web image search results using query-relative classifiers , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Pinar Duygulu Sahin,et al.  Re-ranking of web image search results using a graph algorithm , 2008, 2008 19th International Conference on Pattern Recognition.

[15]  Wei Liu,et al.  Noise resistant graph ranking for improved web image search , 2011, CVPR 2011.

[16]  Boris Babenko,et al.  ImprovingWeb-based Image Search via Content Based Clustering , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[17]  Berthier A. Ribeiro-Neto,et al.  Image retrieval using multiple evidence ranking , 2004, IEEE Transactions on Knowledge and Data Engineering.

[18]  Frédéric Jurie,et al.  Visual word disambiguation by semantic contexts , 2011, 2011 International Conference on Computer Vision.

[19]  Pietro Perona,et al.  Learning object categories from Google's image search , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[20]  Christophe Moulin,et al.  Impact of Visual Information on Text and Content Based Image Retrieval , 2010, SSPR/SPR.

[21]  Hervé Glotin,et al.  A Comparative Study of Diversity Methods for Hybrid Text and Image Retrieval Approaches , 2008, CLEF.